This workshop is being held in conjunction with Ubicomp 2012
in Pittsburgh, PA. The paper submission deadline is June 12, 2012.
http://lbsn2012.cmuchimps.org/
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AIMS AND SCOPE
Social networks have been prevalent on the Internet and become a hot
research topic attracting many professionals from a variety of fields.
The advances in location-acquisition and mobile communication
technologies empower people to use location data with existing online
social networks. The dimension of location helps bridge the gap between
the physical world and online social networking services. Furthermore,
people in an existing social network can expand their social structure
with the new interdependency derived from their locations. As location
is one of the most important components of user context, extensive
knowledge about an individual’s interests, behaviors, and relationships
with others can be learned from her locations. These kinds of
location-embedded and location-driven social structures are known as
location-based social networks, formally defined as follows:
A location-based social network (LBSN) does not only mean adding a
location to an existing social network so that people in the social
structure can share location-embedded information, but also consists of
the new social structure made up of individuals connected by the
interdependency derived from their locations in the physical world as
well as their location-tagged media content, such as photos, video, and
texts. Here, the physical location consists of the instant location of
an individual at a given timestamp and the location history that an
individual has accumulated in a certain period.
Further, the interdependency includes not only that two persons co-occur
in the same physical location or share similar location histories but
also the knowledge, e.g., common interests, behavior, and activities,
inferred from an individual’s location (history) and location-tagged data.
In a location-based social network, people can not only track and share
the location-related information of an individual via either mobile
devices or desktop computers, but also leverage collaborative social
knowledge learned from user-generated and location-related content, such
as GPS trajectories and geo-tagged photos. Consequently, LBSNs enable
many novel applications that change the way we live, such as travel
planning, location recommendations, friend suggestion, and community
discovery, while offering many new research opportunities to the
Ubiquitous computing community, including link prediction, human
mobility modeling, and user activity recognition, privacy, and computer
human interaction. Example papers can be found on
http://research.microsoft.com/en-us/projects/lbsn/default.aspx.
The objective of this workshop is to provide professionals, researchers,
and technologists with a single forum where they can discuss and share
the state-of-the-art of LBSN development and applications, present their
ideas and contributions, and set
future directions in emerging innovative research for location
based social networks.
TOPICS OF INTEREST
Topics of interest include, but not limited to, the following aspects:
Understanding users in LBSNs
- User preference modeling
- User mobility modeling and analysis
- Real-world user activity sensing and recognition
- User similarity computing based on locations
- Link prediction and social tiers inference
- Friend recommendations and community discovery
- Expert discovery and influential person identification
- User intention understanding
Understanding locations in LBSNs
- Hot spots, significant places, and interesting locations detection
- Generic or personalized location recommendations
- Popular travel routes discovery from social media
- Trip planning and itinerary suggestion for users
- Location annotation and semantic meaning identification
- Location prediction and location privacy
- Anomaly detection and event discovery from social media
- Trajectory data mining in LBSNs
Information sharing in LBSNs
- Location and location-related data sharing
- Location and location-tagged media visualization
- Human-computer interaction in LBSNs
- Information retrieval in LBSNs.
--
Jason I. Hong, Assoc. Prof. http://www.cs.cmu.edu/~jasonh
HCI Institute, School of Computer Science, Carnegie Mellon University
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